4 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
Sparse Model Inversion: Efficient Inversion of Vision Transformers for Data-Free Applications
Zixuan Hu, Yongxian Wei, Li Shen +4
Model inversion, which aims to reconstruct the original training data from pre-trained discriminative models, is especially useful when the original training data is unavailable du…
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
Zixuan Hu, Yongxian Wei, Li Shen +2
Large Language Models (LLMs) such as ChatGPT demonstrate strong few-shot adaptability without requiring fine-tuning, positioning them ideal for data-limited and real-time applicati…
Exploring Model Transferability through the Lens of Potential Energy
Xiaotong Li, Zixuan Hu, Yixiao Ge +2
Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models. However, selecting the optimal pre-trained model fro…
Modeling Uncertain Feature Representation for Domain Generalization
Xiaotong Li, Zixuan Hu, Jun Liu +3
Though deep neural networks have achieved impressive success on various vision tasks, obvious performance degradation still exists when models are tested in out-of-distribution sce…
mc-BEiT: Multi-choice Discretization for Image BERT Pre-training
Xiaotong Li, Yixiao Ge, Kun Yi +3
Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a class…